System and method for facilitating dbs electrode trajectory planning

ABSTRACT

A system and method for facilitating DBS electrode trajectory planning using a machine learning (ML)-based feature identification scheme configured to identify and distinguish between various regions of interest (ROIs) and regions of avoidance (ROAs) in a patient&#39;s brain scan image. In one arrangement, standard orientation image slices as well as re-sliced images in non-standard orientations are provided in a labeled input dataset for training a CNN/ANN for distinguishing between ROIs and ROAs. Upon identification of the ROIs and ROAs in the patient&#39;s brain scan image, an optimal trajectory for implanting a DBS lead may be determined relative to a particular ROI while avoiding any ROAs.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.17/135,022, filed Dec. 28, 2020, the contents of which are incorporatedby reference herein in their entirety and for all purposes.

TECHNICAL FIELD

The present disclosure generally relates to surgical procedures. Moreparticularly, and not by way of any limitation, the present disclosureis directed to a system and method for facilitating electrode trajectoryplanning in deep brain stimulation (DBS) therapy applications.

BACKGROUND

Deep brain stimulation (DBS) refers to the delivery of electrical pulsesinto one or several specific sites within the brain of a patient totreat various neurological disorders. For example, deep brainstimulation has been proposed as a clinical technique for treatment ofchronic pain, essential tremor, Parkinson's disease (PD), dystonia,epilepsy, depression, obsessive-compulsive disorder, and otherdisorders.

Brain anatomy typically requires precise targeting of tissue forstimulation by deep brain stimulation systems. For example, deep brainstimulation for Parkinson's disease commonly targets tissue within orclose to the subthalamic nucleus (STN). The STN is a relatively smallstructure with diverse functions. Stimulation of undesired portions ofthe STN or immediately surrounding tissue can result in negative sideeffects. Mood and behavior dysregulation and other psychiatric effectshave been reported from inaccurate stimulation of the STN in Parkinson'spatients.

A deep brain stimulation procedure typically involves first obtainingpreoperative images of the patient's brain (e.g., using computedtomography (CT) or magnetic resonance imaging (MRI)). Using thepreoperative images, the neurosurgeon can select a target region withinthe brain, an entry point on the patient's skull, and a desiredtrajectory between the entry point and the target region. Because of themanual nature of identifying the target regions on a patient-by-patientbasis, the existing procedures are usually time-consuming and expensive.Further, visual identification of potential target regions is ofteninaccurate, especially when the intended target is small and contains3-dimensional (3D) functional subsystems that are hard to visualize on2D images. Such challenges in combination with operation error can leadto incorrect implantation of a DBS electrode in the patient.

SUMMARY

Example embodiments of the present patent disclosure are directed to asystem and method for facilitating DBS electrode trajectory planningusing a machine learning (ML)-based feature identification scheme thatadvantageously identifies and distinguishes between various regions ofinterest (ROIs) and regions of avoidance (ROAs) in a patient's brainscan image on an individualized basis with minimal error in an efficientand robust manner while being readily generalizable and applicable tomost patients.

In one aspect, an embodiment of a computer-implemented method ofelectrode trajectory planning for DBS therapy is disclosed. The claimedembodiment may comprise, inter alia, obtaining a set of medical imagingdata pertaining to human cranial anatomy, the set of medical imagingdata sampled from a plurality of humans in one or more imagingmodalities, wherein the medical imaging data comprises image slices ofthe human brain taken along at least one of coronal, sagittal andtransverse or axial planes relative to the human cranial anatomy. Theclaimed embodiment may further include effectuating one or more dataaugmentation and preprocessing techniques including, without limitation,re-slicing at least a portion of the medical imaging data through one ormore planes that are at an angular orientation with respect to at leastone of the coronal, sagittal and axial planes, thereby obtainingre-sliced medical imaging data. In one arrangement, one or more trainingdatasets, validation datasets and/or testing datasets may be generatedthat may include at least a portion of the standard orientation imageslices of the medical imaging data as well as the augmented/preprocessedmedical imaging data, wherein the various structures of interest (e.g.,ROAs, ROIs, etc), or collectively “targets,” in general, may beappropriately labeled by human experts. According to some embodiments, aconvolutional neural network (CNN) and/or an artificial neural network(ANN) based on deep learning techniques may be implemented as anML-based image/feature identification scheme. In one arrangement, afirst ANN engine may be trained using a portion of the labeled medicalimaging data that has not been re-sliced and a portion of the re-slicedmedical imaging data to obtain a validated and tested ANN engineconfigured to distinguish between one or more regions of interest (ROIs)from one or more regions of avoidance (ROA) in a human brain image. Inone arrangement, the first ANN engine may be applied or executed inresponse to an input image of a patient's brain obtained using aparticular imaging modality in order to identify at least one particularROI in the patient's brain to facilitate planning of an optimaltrajectory for implanting a DBS lead having one or more electrodes inthe at least one particular ROI while avoiding any ROAs identified inthe patient's brain.

In one variation, a data augmentation technique may involve blending twoor more co-registered image slices selected from the standardorientation medical imaging data and/or re-sliced imaging data to obtainhybrid image slices. In one variation, one or more of the training,validation and test datasets may be populated with the hybrid imageslices in order to enhance the predictive power of a trained ANN engineoperative as an embodiment of the ML-based image/feature identificationscheme.

In a further variation, after obtaining, identifying or otherwisepredicting the structures of interest in the patient's brain image, an“electrode scene” may be built wherein a representation of a DBSelectrode may be automatically generated relative to an optimaltrajectory that targets the ROI. In one arrangement, path data relatingto the optimal trajectory may be provided to a stereotactic surgerysystem that may use pre-operative CT image data for facilitating a DBSimplant procedure.

In another aspect, an embodiment of a computer-implemented system ofelectrode trajectory planning for DBS therapy is disclosed, whichcomprises, inter alia, one or more processors, one or more inputdatasets including (pre)processed and augmented image data, and apersistent memory having program instructions stored thereon, whereinthe program instructions, when executed by the one or more processors,are configured to perform one or more methods set forth herein.

In still further aspects, a non-transitory computer-readable medium ormedia containing computer-executable program instructions or codeportions stored thereon is disclosed for performing example methodsherein when executed by a processor entity of an apparatus.

Additional/alternative features and variations of the embodiments aswell as the advantages thereof will be apparent in view of the followingdescription and accompanying Figures.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the present disclosure are illustrated by way of example,and not by way of limitation, in the Figures of the accompanyingdrawings in which like references indicate similar elements. It shouldbe noted that different references to “an” or “one” embodiment in thisdisclosure are not necessarily to the same embodiment, and suchreferences may mean at least one. Further, when a particular feature,structure, or characteristic is described in connection with anembodiment, it is submitted that it is within the knowledge of oneskilled in the art to effectuate such feature, structure, orcharacteristic in connection with other embodiments whether or notexplicitly described.

The accompanying drawings are incorporated into and form a part of thespecification to illustrate one or more exemplary embodiments of thepresent disclosure. Various advantages and features of the disclosurewill be understood from the following Detailed Description taken inconnection with the appended claims and with reference to the attacheddrawing Figures in which:

FIG. 1A depicts an example human brain anatomical coordination systemfor use in reference to standard orientation medical imaging data and/orprocessed medical imaging data wherein image slices may be obtainedthrough multiple planes and orientations for purposes of someembodiments of the present patent disclosure;

FIGS. 1B-1 to 1B-3 depict example standard orientation sectional imagesin one imaging modality;

FIGS. 1C-1 to 1C-3 depict example standard orientation sectional imagesin another imaging modality;

FIGS. 1D-1 to 1D-3 depict example re-sliced sectional images (e.g.,along a non-standard orientation or plane) in one imaging modality;

FIGS. 2A-2G depict flowcharts illustrative of blocks, steps and/or actsthat may be (re)combined in one or more arrangements with or withoutadditional flowcharts of the present patent disclosure for facilitatinga machine learning (ML)-based image identification scheme foridentifying regions of interest (ROIs) and regions of avoidance (ROAs)in connection with electrode trajectory planning according to someembodiments of the present patent disclosure;

FIGS. 3A and 3B depict generalized artificial neural network (ANN) andconvolution neural network (CNN) models operative as an ML-based processor engine for identifying or predicting ROIs and ROAs according to someembodiments of the present patent disclosure;

FIG. 4 depicts a block diagram of an apparatus, node, or a computingplatform for training, testing and generating a validated/tested CNN/ANNprocess or engine operative in conjunction with a stereotactic surgerysystem for purposes of an example embodiment of the present patentdisclosure;

FIG. 5 depicts a block diagram of a system involving a plurality ofmodules that may be configured as an integrated or distributed platformfor effectuating ML-based electrode trajectory planning according to anexample embodiment of the present patent disclosure;

FIG. 6 depicts an automated stereotactic surgery system for facilitatinga DBS electrode implant procedure according to an example embodiment ofthe present patent disclosure;

FIGS. 7A and 7B depict a DBS pulse generator and associated lead systemhaving a plurality of electrodes that may be implanted using an imageidentification system according to an example embodiment of the presentpatent disclosure; and

FIG. 8 is an illustrative diagram of a patient having one or moretherapy leads implanted in different brain regions using a trajectoryguide apparatus aided by an embodiment of the present disclosureaccording to the teachings herein.

DETAILED DESCRIPTION

In the description herein for embodiments of the present disclosure,numerous specific details are provided, such as examples of circuits,devices, components and/or methods, to provide a thorough understandingof embodiments of the present disclosure. One skilled in the relevantart will recognize, however, that an embodiment of the disclosure can bepracticed without one or more of the specific details, or with otherapparatuses, systems, assemblies, methods, components, materials, parts,and/or the like set forth in reference to other embodiments herein. Inother instances, well-known structures, materials, or operations are notspecifically shown or described in detail to avoid obscuring aspects ofembodiments of the present disclosure. Accordingly, it will beappreciated by one skilled in the art that the embodiments of thepresent disclosure may be practiced without such specific components. Itshould be further recognized that those of ordinary skill in the art,with the aid of the Detailed Description set forth herein and takingreference to the accompanying drawings, will be able to make and use oneor more embodiments without undue experimentation.

Additionally, terms such as “coupled” and “connected,” along with theirderivatives, may be used in the following description, claims, or both.It should be understood that these terms are not necessarily intended assynonyms for each other. “Coupled” may be used to indicate that two ormore elements, which may or may not be in direct physical or electricalcontact with each other, co-operate or interact with each other.“Connected” may be used to indicate the establishment of communication,i.e., a communicative relationship, between two or more elements thatare coupled with each other. Further, in one or more example embodimentsset forth herein, generally speaking, an electrical element, componentor module may be configured to perform a function if the element may beprogrammed for performing or otherwise structurally arranged to performthat function.

FIG. 1A depicts an example human brain anatomical coordinate system foruse in reference to medical imaging data that may be obtained throughand/or along multiple planes and orientations with respect to humancranial anatomy in one or more imaging modalities, wherein image slicesmay be processed, preprocessed, or otherwise manipulated for purposes ofsome embodiments of the present patent disclosure. In general, imagingmodalities may include but are not limited to: T1-weighted magneticresonance imaging (MRI) pre-contrast, T1-weighted MRI post-contrast,T2-weighted MRI, MRI sequence with susceptibility-weighted imaging(SWI), diffusion tensor imaging (DTI) tractography, MR-angiography,MR-elastography, computed tomography (CT), etc. Although some exampleembodiments set forth below may be particularly described in referenceto certain MRI-based modalities, it should be appreciated that theteachings herein are not necessarily limited thereto. In somearrangements, the medical imaging data of human brain anatomy may beobtained in one or more standard orientations relative to the threeprincipal axes of a human body, e.g., latero-lateral (X), dorso-ventral(Y) and rostro-caudal (Z) axes, that give rise to three standard planesor sections with respect to human brain 109: a sagittal or medialsection 102A, a coronal section 102B, and an axial or transverse section102C as illustrated in image coordinate system 100A. A brain atlasdatabase may be composed of images of several serial sections alongdifferent anatomical planes, wherein each relevant brain structure maybe assigned a label and a number of coordinates to define its outline orvolume. Images of serial sections taken along a standard anatomicalplane are parallel to one another (e.g., having a uniform inter-sliceseparation), which may be referred to as standard slices, e.g., sagittalslices 104A, coronal slices 104B and axial slices 104C. In somearrangements, to facilitate comparative group analyses using functionalimaging data, inter alia, the individual brain images may be transformedinto a common coordinate space such as, e.g., the Talairach space, theMontreal Neurological Institute (MNI) space, etc., as known in the art.

As will be seen further below, example embodiments may be configured toutilize a variety of data conditioning and/or image processingtechniques and processes with respect to the standard medical imagingdata, which may be used to generate one or more conditioned datasets fortraining, validating, and testing a suitable machine learning (ML)-basedimage identification/prediction process or engine for facilitating areliable and effective DBS electrode trajectory planning scheme thatovercomes the deficiencies and shortcomings of existing technologies asset forth elsewhere in the present patent disclosure. An example(pre)processing technique for purposes of some embodiments may involve“re-slicing” of one or more standard slices, e.g., image slices 104A-C,wherein a standard slice may be rotated or “visualized” in one or moredifferent perspectives relative to the standard anatomical planes and/orthe principal axes, whereby additional features, aspects, properties, orother image qualities of a brain structure of interest may be revealedin a fashion so as to enhance the input dataset quality. By way ofillustration, a non-standard slice 103 is exemplified in FIG. 1 as are-slice generated from a standard coronal slice having an angularorientation with respect to the coronal plane 102B. As will be apparentfrom the description further below, by improving the input datasetquality via re-slicing and other techniques (which may be collectivelyreferred to as data augmentation or data perturbation) for purposes oftraining, validation and testing, a more efficient ML engine may begenerated according to some example embodiments of the present patentdisclosure.

In one arrangement, an example embodiment may therefore be configured toinclude the following processes, components, or subsystems:conditioning/preprocessing of medical imaging data; training, validatingand testing an ML engine based on deep learning techniques, e.g., one ormore artificial neural networks (ANNs) or convolutional neural networks(CNNs), hereinafter cumulatively and generally referred to as ANN or CNNin an interchangeable manner, unless otherwise specifically noted, thatis operative as an intelligent image feature prediction system toidentify various targets of interest (TOIs, also referred to as regionsof interest or ROIs, or volumes of interest or VOIs) and targets ofavoidance (TOAs, also referred to as regions of avoidance or ROAs, orvolumes of avoidance or VOAs) based on available imaging data; applyingor executing the trained ML engine with respect to a subject patient'simage data to predict or otherwise identify particular ROIs and/or ROAstherein; and planning a optimal trajectory using the ROI/ROA locationdata for an electrode implant procedure. Optionally, where a CT systemis incorporated, an example embodiment may be configured to provideappropriate coordinate information to a stereotactic surgery system tofacilitate the implant procedure either automatically and/or under theguidance of a surgeon. Skilled artisans will recognize that example ROIsmay include, but are not limited to, the subthalamic nucleus (STN),internal globus pallidus (GPi), external GP (GPe), thalamus, anteriorcingulate cortex, etc., which may be dependent on the type ofstimulation therapy contemplated. In similar fashion, example ROAs mayinclude but are not limited to brain structures such as, e.g., bloodvessels, ventricles, and white matter tracts, etc.

FIGS. 1B-1 to 1B-3 depict example standard orientation sectional imagesin one imaging modality using T2-MRI that illustrate representativeROI(s) in multiple perspectives. In particular, image 100B-1 shows anexample volume 152 containing STN and other basal ganglia nuclei in asagittal section, image 100B-2 shows example volume 152 in a coronalsection, and image 100B-3 shows example volume 152 in an axial section.FIGS. 1C-1 to 1C-3 depict example standard orientation sectional imagesin another imaging modality using T1-MRI that illustrate example volume152 in a sagittal section 100C-1, a coronal section 100C-2 and an axialsection 100C-3, respectively. FIGS. 1D-1 to 1D-3 depict examplere-sliced sectional images (e.g., along a non-standard orientation orplane) in one imaging modality, e.g., T2-MRI. In particular, re-slicingmay be performed along a primary axis plane in reference to a 3Dcoordinate system defined based on the location of a particular portionof example volume 152, e.g., STN 166, in addition to the planesorthogonal thereto. By way of example, a primary axis 162 is illustratedin image 100D-1 of FIG. 1D-1 . Images 100D-2 and 100D-3 are sectionalimages relative to image 100D-1, shown respectively in FIGS. 100D-2 and100D-3 , each depicting STN 166 in a non-standard perspective. It willbe apparent that various ROAs may also be identified and/or labeled in asimilar manner in the foregoing sectional images regardless of theorientations.

FIGS. 2A-2G depict flowcharts illustrative of blocks, steps and/or actsthat may be (re)combined in one or more arrangements with or withoutadditional flowcharts of the present patent disclosure for facilitatingML-based image identification for identifying ROIs and ROAs inconnection with electrode trajectory planning according to someembodiments of the present patent disclosure. Example process 200A ofFIG. 2A may commence with obtaining a set of medical imaging datapertaining to human cranial/brain anatomy, wherein the medical imagingdata comprises image slices taken along at least one of coronal,sagittal and transversal planes relative to the human brain anatomy(block 202). In one arrangement, the medical imaging data may beobtained from a plurality of humans in one or more imaging modalitiesthat may be publicly available (e.g., as a brain atlas database). Inanother arrangement, the medical imaging data may comprise proprietaryinformation. At block 204, a variety of data augmentation/pre-processingtechniques, e.g., including re-slicing of the medical imaging data, maybe performed. As previously noted, re-slicing may be performed withrespect to each of the standard slices or at least a portion thereof ofthe medical imaging data, wherein re-sliced images through one or moreplanes that are at an angular orientation with respect to at least oneof the coronal, sagittal and axial planes may be generated, therebyobtaining a re-sliced medical imaging data portion. Because a standardimage slice may be angularly and/or privotally rotated around aprincipal axis in a variety of orientations, theoretically an infinitenumber of re-sliced images may be obtained for each standard imageslice. It will be appreciated, however, that additional usefulinformation that may be obtained from generating extra re-slices mayprogressively diminish. Accordingly, due to practical utilityconsiderations, only a relatively small number of re-sliced images maybe generated with respect to a particular standard slice in an exampleembodiment. Further, the re-slicing does not have to be uniformthroughout the human cranial anatomy, e.g., a re-sliced image may be ata higher resolution near the ROIs, and preferably in orientations thatenhances the target view. In a further arrangement, a re-slicingorientation may be selected based on the ROIs, e.g., slices parallel toSTN, GPi, etc. An example embodiment may therefore be advantageouslyconfigured such that appropriate re-slicing may be performed not only toensure that various ROIs/ROAs are visible from multiple angles forfacilitating the training of an ML-based image identification andprediction engine in a more efficient way but also to help enhance thepredictive accuracy of the resultant ML engine.

In one arrangement, deep learning techniques (e.g., ANNs, CNNs, etc.)may be employed to facilitate automatic segmentation of ROIs and ROAs ofhuman brain images, as previously noted. Depending on implementation,example neural networks may be configured to utilize parameterized,sparsely connected kernels which preserve the spatial characteristics ofimages. An example CNN may be trained, validated and tested on asuitably conditioned input dataset (e.g., a training dataset) in orderto classify the pixels of input images into various ROIs such as thesubthalamic nucleus, globus pallidus, thalamus, anterior cingulatecortex, etc., as noted above. It will be apparent to one skilled in theart upon reference hereto that the various ROIs and ROAs may beappropriately labeled in the input dataset images by human experts(e.g., neuroradiologists) and/or artificial intelligence (AI)-basedexpert systems, wherein the input dataset(s) may be partitioned intoseparate training, validation and testing datasets with correctlabeling. Furthermore, such training datasets may include standardslices as well as re-sliced images obtained from different imagingmodalities including but not limited to T1-weighted MRI pre-contrast,T1-weighted MRI post-contrast, T2-weighted MRI, SWI, DTI, tractography,MR-angiography, MR-elastography, CT, etc., as previously noted,including other imaging technologies such as isocentric fluoroscopy,bi-plane fluroscopy, MRI-associated 4D imaging (e.g., using movement orflow as the fourth dimension), optical coherence tomography (OCT),planar gamma scintigraphy (PGS), functional imaging techniques such aspositron emission tomography (PET), single-photon emission computerizedtomography (SPECTscan), functional MRI, magnetic source imaging (MSI) ormagnetoencephalography (MEG), and the like. Some embodiments may involvestill further types of image acquisition technologies such as3D-transcranial ultrasound (TCU), contrast enhanced TCU, and so on. Aswill be seen further below, one or more CNNs/ANNs may be may begenerated and trained on various types of training datasets, validationdatasets and testing datasets, wherein appropriate data augmentationtechniques may be applied depending on the imaging modalities.

In some embodiments, a first ANN engine may be trained using a portionof the medical imaging data that has not been re-sliced as well as aportion comprising the re-sliced medical imaging data to obtain avalidated and tested ANN engine configured to distinguish between one ormore ROIs from one or more ROAs in a human brain image, as set forth atblock 206. At block 208, the first validated/tested ANN engine may beexecuted, for example, in response to an input image of a patient'sbrain obtained using a particular imaging modality, to identify at leastone particular ROI in the patient's brain to facilitate planning of anoptimal trajectory for implanting a DBS lead with one or more electrodesin the at least one particular ROI while avoiding any ROAs identified inthe patient's brain.

In some example embodiments, one or more hybrid image blendingtechniques may be employed as part of data augmentation and conditioningwith respect to the input image datasets used for training, validationand testing of a CNN engine. For example, two or more medical images maybe blended into “hybrid” images and used as additional input data fortraining the CNN engine. Alternatively and/or additionally, an entirelynew CNN engine may be trained to exclusively recognize such hybridimages. In such an arrangement, the predicted segmentation results(i.e., separated sets of ROIs and ROAs in an image) from thehybrid-imaging CNN engine may be combined with the segmentation resultsproduced by the standard-imaging CNN engine to improve predictionresults.

In some arrangements, the exact type of image blending may depend on theavailable data types, imaging modalities, particular ROI(s)/ROA(s), or acombination thereof. For example, a weighted sum of T1 and T2 MRI slicesmay be used (e.g., as a linear weighting) to create a T1-T2 hybridimage. If the targets are ROAs, e.g., the white matter tracks, anexample embodiment may be configured to weigh the T1 image more heavily.On the other hand, if the target is a nucleus such as the STN (which isan ROI), the T2 image may be accorded a heavier weight in one exampleembodiment. If SWI images are available, they can also be added to thelinear sum (and provided a heavier weight) in one arrangement torecognize nuclei such as STN. In addition to weighted linear summation,a ratio of two modalities (such as T1 and T2) can also be used tohighlight specific features such as ventricles or myelination.

Accordingly, it should be appreciated that hybrid images can be acombination of any modalities depending on implementation, e.g., MRI+CT;MRI+CT+DTI; MRI+X-ray, etc. Further, the resolution of images may beup-sampled or down-sampled to combine in some arrangements. In stillfurther arrangements, an example embodiment may be configured to combineand blend images that are co-registered and in the same resolution after(pre)processing. Although it is theoretically possible to combine imagesthat are not in the same orientation or not co-registered (i.e., lineartranslation), such blending may not yield hybrid images that aresufficiently informative, and therefore may not be implemented in somearrangements. Moreover, both standard orientation images as well asre-sliced images may be blended to generate hybrid images in differentcombinations that may be used as part of any of the training, validationand/or testing datasets according to some embodiments of the presentpatent disclosure.

Example process 200B shown in FIG. 2B is illustrative of some of theforegoing variations that may be practiced optionally according to someembodiments. At block 212, two or more co-registered and/orco-resolution image slices may be blended, e.g., selected from either aportion of the medical imaging data that has not been re-sliced and/orfrom the portion comprising the re-sliced medical imaging data, in orderto generate one or more hybrid image slices. At block 214, at least aportion of the hybrid image slices may be used in training, validatingand testing the first ANN engine and/or a second ANN engine. Where aseparate second ANN engine is utilized, the first and second ANN enginesmay be executed separately with respect to an input image of a patient'sbrain to obtain two separate sets of ROI and ROApredictions/identifications (block 216). Thereafter, the separate setsof ROIs and ROA predictions/identifications may be combined to obtainhigher quality predictions/identifications, as set forth at block 218.

In some arrangements, input image datasets may be comprised of binaryimages that may contain certain imperfections that need to be rectifiedprior to using in ML training. In particular, the binary regionsproduced in an image by simple thresholding may be distorted by noiseand texture. Some embodiments of the present patent disclosure maytherefore employ various morphological image processing techniques aspart of data augmentation and conditioning. As set forth at block 222,an example process 200C shown in FIG. 2C may involve, prior to trainingeither a first and/or second ANN engines (e.g., where a separate ANNengine is provided with respect to blended images as set forth above),morphological image processing of image slices with respect to specificROI/ROA features e.g., edge detection, contrast boosting,shape/structure detection, etc. At block 224, example process 200C mayuse the processed image slices in training, validating and testing thefirst and/or second ANN engines. An example embodiment may therefore beconfigured to employ morphological image processing as a collection ofnonlinear operations related to the shape or morphology of features inan image. In some embodiments, such morphological operations mayprimarily rely on the relative ordering of pixel values, not on theirnumerical values, and may therefore be especially suited to theprocessing of binary images. Morphological operations can be applied tograyscale images as well as colorized images (e.g., false coloring),depending on the imaging modality. In one arrangement, an examplemorphological operations may comprise erosion, dilation, opening andclosing with respect to sets of pixels using such concepts as size,shape, convexity, connectivity, and geodesic distance, etc.

According to some embodiments, therefore, any combination of theforegoing techniques may be used on any of the images or blended images,regardless of whether the images comprise re-sliced images or are ofstandard orientation, in order to highlight the ROIs/ROAs. Suchprocessed images may be re-sliced and input as part of thetraining/testing of the ANN engine, or developed into a separate ANNengine to enhance prediction, similar to the use of a second ANN enginewith respect the blended images as described above.

In still further arrangements, certain additional and/or alternativedata augmentation and/or generalization techniques may be selectively oroptionally applied on the training datasets. For example, techniquessuch as directional slicing may be used as a data perturbation processto enhance the input data pool for the neural network modeling andtraining. It should be appreciated that feeding input data images in allorientations (2D, 3D, or both) can help ensure that the ANN engine has abetter representation of the targets. Further, in order to prepare thenetwork for noisy data and possible overlap of targets, a dropouttechnique may be applied in an example embodiment. As exemplified atblock 226 of example process 200D illustrated in FIG. 2D, a dropouttechnique may be performed with respect to any of the ANN engines,(e.g., the first and/or second ANN engines described above), wherein aselect number of neurons or nodes are dropped from a particular neuralnetwork layer for each training iteration or epoch. In particular, sucha scheme is operative to remove from weights calculation a random numberof connections, which may change for each training pass (i.e., epoch) ofan ANN engine, as will be set forth further below.

Additionally, if multiple datasets are available for the same subject,multiple representations of the same structure can be presented andlabeled on the different sets of data. For example, blood vessels can beidentified on T1-weighted post-contrast MRI as well as T2-weighted MRIof the same subject. In one embodiment, all the classes/labels may beused to train the neural network. In still further embodiments,techniques such as transfer learning (a machine learning process where amodel developed for a task is reused as the starting point for a modelon a second task) may be employed to utilize a pre-trained neuralnetwork on other brain data (such as detection of brain tumor), unfreezethe last few layers of the hidden layers, and train on a dataset to beused for DBS electrode trajectory planning. Skilled artisans willrecognize that employing such transfer learning techniques may help aneural network for DBS electrode trajectory planning converge faster andperform ROA/ROI predictions with higher accuracy.

Example processes 200E-200G shown in FIGS. 2E-2G, respectively, areillustrative of additional and/or alternative aspects that may beselectively or optionally implemented in some embodiments of the presentpatent disclosure. In some arrangements, for example, a virtual“electrode scene” may be built with respect to a patient's brain imageafter obtaining the prediction(s) of ROls/ROAs therein for placing a DBSlead therein, as set forth at block 232 of process 200E, wherein anexample DBS lead may comprise one or more electrodes that may besegmented or otherwise, as will be set forth in further detail below. Inone implementation, a representation of a selected electrode (e.g., agraphic symbol, pictorial image, pictogram, or an icon, etc. associatedtherewith) may be automatically generated at a path that hits orterminates at a specific location of a selected ROI (e.g., the center ofthe target region) while avoiding the ROAs such as, e.g., blood vessels,ventricles, etc. Given the respective locations of the ROls and ROAsidentified in reference to a known coordinate system, any known orheretofore unknown path optimization process may be executed tocalculate, obtain, estimate or otherwise determine an optimaltrajectory, as set forth at block 234. Additionally, alternatively oroptionally, the user (e.g., a clinician or a medical technician) mayspecify the location of a certain electrode, or a segment (or segments)thereof, in relation to a particular target of interest by modifying theautomatically generated electrode scene. Responsive thereto, the pathoptimization process may be configured to recalculate the path andupdate the trajectory accordingly.

In an arrangement where a CT imaging system is configured tointeroperate with an example ML-based image identification system of thepresent patent disclosure as part of a stereotactic surgery systemincluding a trajectory guiding apparatus, additional features andaspects may be implemented according to some embodiments herein. Forexample, if a pre-operational CT scan of a patient's brain is available,the CT scan may be co-registered to the patient's MRI scan as set forthat block 242 of process 200F shown in FIG. 2F. Depending on thestereotactic system used, an embodiment may be configured to output oneor more coordinates for facilitating the stereotactic surgical procedurewith respect to implanting a DBS lead. For example, an entry pointcoordinate set and a target point coordinate set with respect to thepatient's brain may be obtained and/or generated for performing animplant procedure to implant the DBS lead using the optimal trajectory.As one skilled in the art will recognize, the entry point coordinate setmay be operative to identify a burr hole location on the patient'scranium and the target point coordinate set may be operative to identifya location relative to at least one particular ROI in the patient'sbrain, as set forth at block 244. Further, the trajectory datacomprising the entry point coordinate set, target point coordinate set,as well as any data relating to the optimal trajectory that may beconverted to data suitable for controlling and/or configuring a guidingapparatus may be provided in real time to the stereotactic surgicalstation, as set forth at block 252 of process 200G shown in FIG. 2G.Responsive thereto, the DBS lead may be automatically guided andadvanced to the ROI based on the target coordinate data to place theparticular electrode proximate to the ROI at the indicated location(block 254).

Although an example implementation of the foregoing arrangement may beconfigured to be fully automatic, human intervention (e.g., a clinicianor a medical technician) can be applied at various steps of targetsegmentation and trajectory planning (e.g., to fine-tune a leadtrajectory in situ). In some embodiments, the ML-building process of thepresent patent disclosure may be supervised such that any humaninteractions to alter or otherwise modify a calculated path may beprovided as real time feedback whereby the learning aspect of the ANNengine may be triggered for iteratively improving the ANN performancebased on user preference and in situ adjustment information.

FIGS. 3A and 3B depict generalized ANN and CNN models operative as anML-based process or engine for identifying or predicting ROIs and ROAsaccording to some embodiments of the present patent disclosure. Asexemplified, ANN model 300A is operative in response to a plurality ofpixels or pixel groups of an image having a plurality of labeledfeatures, structures or regions that require identification. Inputpixels may be provided to a corresponding input “neuron” orcomputational node 310-1 to 310-N that forms part of an input layer 302.Typically, ANN model 300A may be configured such that the nodes of theinput layer 302 are passive, in that they do not modify the data.Rather, they receive a single value on their input, and duplicate thevalue to their respective multiple outputs, which may depend on theconnectivity of the ANN model 300A. One or more hidden layers 304 may beprovided for reducing the dimensionality of the input feature parametricspace, wherein each of hidden nodes 312-1 to 312-K and 314-1 to 314-Mare active, i.e., they modify the incoming data received from the priorlayer nodes and output a value based on a functional computationinvolving weighted incoming data. In a fully interconnected ANNstructure, each value from an input layer may be duplicated and sent toall of the hidden nodes. Regardless of the extent of theinterconnectivity, the values entering a hidden node at any given hiddenlayer may be multiplied by weights, which comprise a set ofpredetermined numbers stored in the engine that may be “learned” in aseries of iterative stages involving, e.g., output error backpropagation or other methodologies. At each respective hidden node, theweighted inputs are added to produce a single intermediate value, whichmay be transformed through a suitable mathematical function (e.g. anonlinear function, also referred to as an activation function) togenerate an intermediate output between a normalized range (e.g.,between 0 and 1). Depending on the number of hidden layers, weightedintermediate outputs may be provided to a next layer, and so on, untilreaching an output layer 320 comprising one or more output nodes 306-1to 306-L, which may be configured as a vector of values or probabilitiesidentifying or predicting to which class or labeled structure a group ofpixels belong.

Whereas neural networks can have any number of layers, and any number ofnodes per layer, an example ANN model 300A may be configured with afairly small number of layers, comprising only a portion of the size ofthe input layer. In the example arrangement shown in FIG. 3A, two hiddenlayers and an active output layer are shown, with inputs to first andsecond hidden layer nodes being modulated by weights {Wi,j} and {Wk,l}respectively, and inputs to the output node being modulated by weights{Wm,n}. The weights required to make example ANN model 300A to carry outa particular task, e.g., feature/image recognition, may be found by alearning algorithm, together with examples of how the system shouldoperate in certain implementations.

As noted above, a fully-connected ANN engine involves every neuron inone layer connecting to all the neurons in the next layer. It is known,however, that a fully-connected network architecture is inefficient whenit comes to processing image data because of various reasons. Forexample, for an average image with hundreds of pixels and two or threecolor channels, a traditional neural network may generate millions ofparameters, which can lead to overfitting in addition to giving rise tocomputational inefficiency. Furthermore, it may be difficult tointerpret results, debug and tune the model to improve its performancein an efficient manner. Accordingly, some embodiments may involve anoverfitting-prevention technique such as a dropout technique in traininga neural network, which randomly selects a subset of neurons and dropsthe rest neurons of a certain NN layer for each training epoch. Thisensures that each epoch will be trained with a different set of neurons,and therefore regularizes the model and help prevent overfitting.

Unlike a fully connected neural network, a CNN engine (also sometimesreferred to as a ConvNet) may be configured in which the neurons in onelayer do not connect to all the neurons in the next layer. Rather, a CNNengine uses a three-dimensional structure, where each set of neuronsanalyzes a specific region or “feature” of the image. In onearrangement, a CNN model may filter connections by proximity (e.g.,pixels may only be analyzed in relation to pixels nearby), making thetraining process computationally achievable. In a CNN process,therefore, each group of neurons may be configured to focus on one partof the image, e.g., a labeled structure or region. For example, theremay be several stages of segmentation in which the process may beconfigured to smaller parts of the images until a final outputcomprising a vector of probabilities, which predicts, for each featurein the image, how likely it is to belong to a class or category.

Turning to FIG. 3B, depicted therein is an example CNN scheme 300B thatmay be operative as an ML-based image identification process forpurposes of the present patent disclosure according to some embodiments.In operation, a CNN process takes advantage of the fact that the inputconsists of images and they constrain the architecture in a moresensible way. In particular, unlike a regular neural network, the layersof a CNN may have neurons arranged in three dimensions: width, height,and depth, where the term “depth” herein refers to the third dimensionof an activation volume (e.g., depending on the number of color orgrayscale channels), not to the depth of a full neural network, whichcan refer to the total number of layers in a network. Typically, theneurons in a layer will only be connected to a small region of the layerbefore it, instead of all of the neurons in a fully-connected manner, aspreviously noted. Example CNN process 300B may be architected as asequence of layers, wherein every layer is operative to transform onevolume of activations through a differentiable function that may or maynot have parameters. Three main types of layers may be provided inexample CNN process 300B: convolutional layer with activation, poolinglayer, and a fully-connected layer (e.g., similar to a regular neuralnetwork described above), wherein an input image 352 may be sequentiallyprocessed to yield a final output vector of class/label scores. By wayof illustration, one or more convolution/activation layers 354 may beinterspersed with one or more pooling layers 356, which may feed intoone or more fully-connected (FC) layers 358 that generate output 360 ofROI/ROA prediction/identification scores 362-1 to 362-N. A convolutionlayer 354 may be configured to compute the output of neurons that areconnected to local regions in the input or preceding layer, eachcomputing a dot product between their weights and a small region theyare connected to in the input volume. An activation layer associatedwith convolution layer 354 may be configured to apply an element-wiseactivation function such as a rectified linear unit or ReLu (e.g.,max{0,x}). A pooling layer 356 may be operative to perform adown-sampling operation along the spatial dimensions (width and height).An FC layer 358 is operative as an ordinary ANN where each neurontherein is connected to all the members of the previous volume, with thefinal layer generating the ROI/ROA prediction output 360.

In the foregoing manner, example CNN process 300B is operative totransform the original image layer by layer from the original pixelvalues to the final class scores. It should be appreciated that whereassome layers of CNN process 300B may contain parameters, other layer maynot. In particular, the convolution and FC layers performtransformations that are a function of not only the activations in theinput volume, but also of the parameters (e.g., the weights and biasesof the neurons). On the other hand, the activation and pooling layersmay be configured to implement a fixed function. Generally, theparameters in the convolution and FC layers may will be trained with agradient descent process so that the class scores that CNN process 300Bcomputes are consistent with the labels in the training set for eachimage.

Regardless of whether ANNs and/or CNNs are used in an ML-based imagerecognition scheme of the present patent disclosure, certain image datapreprocessing techniques and data augmentation processes may beimplemented to improve the quality of input datasets as notedpreviously. Additional parameters and considerations for image datapreparation may be set forth as below for purposes of some embodiments:

Image size: higher quality images give the model more information butrequire more neural network nodes and more computing power to process.

The number of images: the more data provided to a model, the moreaccurate it will be, while ensuring that the training set represents thereal population.

The number of channels: grayscale images have two channels (black andwhite) and color images typically have three color channels (Red, Green,Blue or RGB), with colors represented in the range [0,255]. Depending onthe image modalities, an example ML-based image recognition system mayuse input data images that may be comprised of grayscale images and/orcolor channels in some embodiments. Further, depending the imagemodality and number of channels, different re-slicing, hybrid imageblending and/or morphological image processing techniques orcombinations thereof may also be employed.

Aspect ratio: ensures that the images have the same aspect ratio andsize. Whereas some embodiments may require input images having a squareshape, it is not necessary for other embodiments of the present patentdisclosure.

Image scaling: as noted previously, input images (comprising standardslices, re-sliced images, hybrid images, etc.) may be up-scaled ordown-scaled using a variety of techniques that may be available as imageprocessing functions in deep learning libraries.

Statistical distributions of input image data: for example, parameterssuch as mean, standard deviation of image data for input pixels may beobtained by calculating the mean values for each pixel, in one or moretraining examples, to obtain information on the underlying structure inthe images.

Normalizing image inputs: ensures that all input parameters (pixels inexample embodiments) have a uniform data distribution. Skilled artisanswill recognize that such data normalization may help achieve fasterrates of convergence when training the ML engine.

Dimensionality reduction: techniques may be applied to collapse the RGBchannels into a grayscale channel in some embodiments. Still furtherembodiments may involve reduction in other dimensions in order to rendertraining less computationally intensive.

Turning attention to FIG. 4 , depicted therein is a block diagram of anapparatus, node, or computing platform 400 for training, testing andgenerating a validated/tested CNN/ANN process or engine operative inconjunction with a stereotactic surgery system for purposes of anexample embodiment of the present patent disclosure. As illustrated, adata collection module 406 is operative to obtain medical imaging datarelating to human cranial anatomy from a number of public and/or privaterepositories 402-1 to 402-K containing image slices in various imagingmodalities. A data preprocessing or cleaning module 408 is operative toperform data cleaning operations as well as data augmentation processes,which may be guided/unguided or supervised/unsupervised by human orAI-based experts 450 having knowledge and domain expertise relative tothe human anatomy and neuroradiology, with respect to the input dataobtained by the data collection module 406. Accordingly, a modifiedimage dataset may be generated by the data preprocessing/cleaning module408, which may be partitioned by an input module 410 as one or moretraining datasets, validation datasets and test datasets. As set forthelsewhere in the present patent disclosure, for purposes of someembodiments herein a training dataset may be a dataset of input imagesor slices (processed, preprocessed or unprocessed) used during thelearning process of a CNN/ANN and is used to fit the parameters of theCNN/ANN. A validation dataset is a dataset of images/slices used to tunethe hyperparameters (i.e., the architecture) of a classifier. A testdataset is a dataset that may be independent of the training dataset butfollows the same probability distribution as the training dataset insome implementations. By way of illustration, a training/validationdataset 412 is exemplified as a labeled dataset that may be provided toa training process 416 with respect to training and validating an MLimage classifier for predicting the ROI/ROA features in human brain scanimages. Depending on the particular ML implementation architecture, MLmodel training 416 may involve one or more iterations, which in someinstances may include (semi)supervised learning based on input fromhuman/AI experts, such that a trained ML model 418 that is appropriatelyfitted is obtained i.e., resulting in a model without underfitting oroverfitting. In one embodiment, the foregoing operations may be providedas part of the ML training stage or aspect of an example implementation.In a subsequent or separate phase, the fitted/trained ML model 418 maybe used in conjunction with additional or partitioned input datasets 414as test data for generating predictive output. As one skilled in the artwill recognize, at least a portion of the foregoing operations may beperformed offline and/or by different computing modules of a distributedcomputing platform depending on implementation. After training, testingand validating the ML model 418, it may be executed in conjunction witha suitable stereotactic surgery system 420 operative for determining animage-guided trajectory based on one or more co-registered pre-operativebrain images 422 of a patient 499.

FIG. 5 depicts a block diagram involving a plurality of modules that maybe configured as a system or apparatus 500 for effectuating ML-basedelectrode trajectory planning according to another view of an exampleembodiment of the present patent disclosure. One or more processors 502may be operatively coupled to various modules that may be implemented inpersistent memory for executing suitable program instructions or codeportions (e.g., code portion 533) with respect to effectuating any ofthe processes, methods and/or flowcharts set forth hereinabove inassociation with one or more modules, e.g., data preprocessing and/oraugmentation module 518, ANN/CNN module(s) 555 (where ML-based imageclassification and feature prediction is implemented), etc. One or moredatabases may be provided as part of or in association with apparatus500 for storing various data, e.g., ML training data 510, ML validationdata 535, ML test data 557, which may be derived fromre-sliced/augmented image data 508 and standard orientation image data559, wherein at least some of the data may be obtained from differentsources and imaging modalities pursuant to federated learning. Althoughnot specifically shown herein, one or more Big Data analytics modulesmay also be interfaced with apparatus 500 for providing additionalpredictive analytics with respect to image classification. Depending onthe implementation and system integration, various network interfaces(I/Fs) 520 may be provided for interfacing with components such asintegrated surgical stations including stereotactic navigation systems,medical imaging systems, external databases, etc.

At least a portion of an ML-based electrode trajectory planning systemdisclosed herein may also be virtualized and/or architected in acloud-computing environment comprising a shared pool of configurableresources deployed as a medical image processing datacenter according tosome embodiments.

FIG. 6 depicts an automated stereotactic surgery system 600 forfacilitating a DBS electrode implant procedure according to an exampleembodiment of the present patent disclosure. An ML-based image predictor620 may be configured to generate an optimal trajectory based onbuilding a virtual electrode scene responsive to analyzing an input MRIbrain image of a patient 602 as described hereinabove. Path/trajectorydata 618 generated by the image predictor 620 may be provided to animage-guided navigation system 612 operated by a clinician or surgeon614, wherein a co-registered CT scan may be used in association with thepatient's MRI image to obtain appropriate coordinates for stereotacticsurgery. Suitable control output may be provided by the image-guidednavigation system 612, automatically or under surgical supervision, toone or more servo motors 610 operative to control a stereotactic frame604 having an implantable instrumentation column 608 containing a DBSlead 606. Although a center-of-arc stereotactic frame 604 is exemplifiedin FIG. 6 , which may encompass the entire head of patient 602, aframeless or microframe arrangement that attaches to only a smallportion of the patient's skull surrounding a burr hole may be providedas a trajectory guiding apparatus in additional or alternativeimplementations for advancing the instrumentation column undernavigational control. As the instrumentation is advanced, its trajectorymay be monitored, e.g., based on tracking on prerecorded imaging or liveimaging, via a display monitor 616. If lateral/axial adjustment withrespect to the trajectory path is needed as the instrumentation isadvanced towards a predetermined target area in the patient's brain,appropriate translational/rotational movement controls of the servomotors 610 may be effectuated accordingly to reorient the stereotacticframe apparatus 604.

FIGS. 7A and 7B depict a DBS therapy system and associated leadarrangement having a plurality of electrodes that may be implanted usingan image identification and trajectory planning system according to anexample embodiment of the present patent disclosure. Example DBS therapysystem 700A includes a pulse generator 702, which may be typicallyimplemented using a metallic housing that encloses circuitry forgenerating one or more electrical pulse sequences for application to thebrain tissue of a patient. Control circuitry, communication circuitry,and a rechargeable battery (not shown) are also typically includedwithin pulse generator 702.

In one arrangement, pulse generator 702 may be configured to wirelesslycommunicate with a programmer device 703. In general operation,programmer device 703 enables a clinician to control the pulsegenerating operations of pulse generator 702. The clinician can selectelectrode combinations, pulse amplitude, pulse width, frequencyparameters, and/or the like using the user interface of programmerdevice 703. As is known in the art, the parameters can be defined interms of “stim sets,” “stimulation programs,” or any other suitableformat. Programmer device 703 responds by communicating the parametersto pulse generator 702 and pulse generator 702 modifies its operationsto generate stimulation pulses according to the communicated parameters.

One or more leads 706 are electrically coupled to the circuitry withinpulse generator 702 using a hermetically sealed header 704. Example lead706 may include terminals (not shown) that are adapted to electricallyconnect with electrical connectors disposed within header 704. Theterminals are electrically coupled to conductors (not shown) within thelead body of lead 706, which conduct pulses from the proximal end to thedistal end of lead 706. The conductors are also electrically coupled toone or more electrodes 708 to apply the pulses to tissue of the patient.Lead 701 can be utilized for any suitable DBS therapy using precisetargeting of tissue for stimulation identified by the electrodetrajectory planning system of the present patent disclosure. Forexample, the distal end of lead 701 may be implanted within a targetlocation for treating a variety of disabling neurological symptoms, mostcommonly the debilitating motor symptoms of Parkinson's disease (PD),such as tremor, rigidity, stiffness, slowed movement, and walkingproblems, as well as conditions such as essential tremor, dystonia,focal epilepsy (epilepsy that originates in just one part of the brain),etc., in addition to mood and anxiety disorders that may be treated bytargeting areas such as nucleus accumbens, subgenual cingulate cortexand ventral capsule/ventral striatum in a patient's brain.

A particularized view 700B of the distal end of lead 706 is illustratedin FIG. 7B, wherein a plurality of electrodes 710-1 to 710-N maycomprise multiple segmented electrodes. It will be appreciated that theuse of segmented electrodes permits the clinician to more preciselycontrol the electrical field generated by the stimulation pulses and,hence, to more precisely control the stimulation effect in surroundingtissue. In some arrangements, electrodes 710-1 to 710-N mayalternatively or additionally include one or more ring electrodes or atip electrode. Any of the electrode assemblies including segmentedand/or unsegmented electrodes discussed herein can be used in a DBSimplant procedure aided by way of an image-guided navigation apparatusoperating in conjunction with the ML-based trajectory planning system ofpresent patent disclosure. The term “segmented electrode” isdistinguishable from the term “ring electrode.” As used herein, the term“segmented electrode” refers to an electrode of a group of electrodesthat are positioned at approximately the same longitudinal locationalong the longitudinal axis of a lead and that are angularly positionedabout the longitudinal axis so they do not overlap and are electricallyisolated from one another. For example, at a given positionlongitudinally along the lead body, three electrodes can be providedwith each electrode covering respective segments of less than 120° aboutthe outer diameter of the lead body. By selecting between suchelectrodes, the electrical field generated by stimulation pulses can bemore precisely controlled and, hence, stimulation of undesired tissuecan be more easily avoided. It should be appreciated, however, thatregardless of the type of electrodes used, the placement and orientationof a select electrode may be better facilitated by obtaining moreaccurate ROI/ROA identification using the ML-based featureidentification and trajectory planning system of the present patentdisclosure. Additional details regarding DBS leads and electrodes, whichmay be used in conjunction with some embodiments herein may be found in,e.g., (i) U.S. Pat. No. 9,238,134, entitled “ELECTRICAL STIMULATIONSYSTEM AND ASSOCIATED APPARATUS FOR SECURING AN ELECTRICAL STIMULATIONLEAD IN POSITION IN A PERSON'S BRAIN”; (ii) U.S. Pat. No. 8,225,504,entitled “MEDICAL LEADS WITH SEGMENTED ELECTRODES AND METHODS OFFABRICATION THEREOF”; and (iii) U.S. Pat. No. 8,463,387, entitled“STIMULATION OF THE AMYGDALOHIPPOCAMPAL COMPLEX TO TREAT NEUROLOGICALCONDITIONS”, each of which is incorporated herein by reference.

FIG. 8 is an illustrative diagram of a patient having one or moretherapy leads implanted in different brain regions using a trajectoryguide apparatus aided by an embodiment of the of the present disclosureaccording to the teachings herein. As illustrated, a patient 852 isshown with two regions or targets of interest 806A and 800B of thepatient's brain 804 that are implanted with respective DBS leads 808Aand 808B, each guided and advanced by a trajectory guide apparatuscontrolled by the ML-based electrode trajectory planning system of thepresent patent disclosure. Prior to the implant procedure, respectiveburr holes 812A and 812B may be drilled in the patient's cranium 802based on the respective entry point coordinate sets obtained asdescribed previously with respect to a desired therapy application,which may be spaced proximate to each other given that an exampletrajectory guide apparatus may be optimized to have a small form factorwhile providing sufficient structural strength to be firmly attached tothe cranium 802. After completion of a suitable burr hole creationprocedure, fiducial markers or reference points may be affixed to thepatient's skull. As noted previously, any suitable imaging technologycan be utilized such as MRI systems, CT systems, etc., for obtainingpre-operative and/or intra-operative imaging data of the patient'sbrain. The imaging may also involve functional analysis of the brain inresponse to specific stimuli. For example, a functional MRI process maybe performed in which stimuli is provided to the patient and the MRIimaging is utilized to identify the specific structures in the brainthat respond to the stimuli. Based upon the imaging information, thetrained ML-based trajectory planning scheme may be executed to providereal time target location information and optimal path data to astereotactic surgery system including the guiding apparatus asdescribed. After completing the guided implantation of leads 808A/808B,the burr holes 812A and 812B may be capped and secured for routing theleads 808A/808B under the scalp of the patient 852. In one arrangement,electrical traces for the leads 808A/808B may be combined into a singlelead body 814 that is routed subcutaneously to be coupled to animplanted pulse generator (IPG) 816, which may be electrically and/ortelemetrically coupled to an external programming system 815 to provideappropriate therapy.

In the above-description of various embodiments of the presentdisclosure, it is to be understood that the terminology used herein isfor the purpose of describing particular embodiments only and is notintended to be limiting of the invention. Unless otherwise defined, allterms (including technical and scientific terms) used herein have thesame meaning as commonly understood by one of ordinary skill in the artto which this invention belongs. It will be further understood thatterms, such as those defined in commonly used dictionaries, should beinterpreted as having a meaning that is consistent with their meaning inthe context of this specification and the relevant art and may not beinterpreted in an idealized or overly formal sense expressly so definedherein.

At least some example embodiments are described herein with reference toone or more circuit diagrams/schematics, block diagrams and/or flowchartillustrations. It is understood that such diagrams and/or flowchartillustrations, and combinations of blocks in the block diagrams and/orflowchart illustrations, can be implemented by any appropriate circuitryconfigured to achieve the desired functionalities. Accordingly, exampleembodiments of the present disclosure may be embodied in hardware and/orin software (including firmware, resident software, micro-code, etc.)operating in conjunction with suitable processing units ormicrocontrollers, which may collectively be referred to as “circuitry,”“a module” or variants thereof. An example processing unit or a modulemay include, by way of illustration, a general purpose processor, aspecial purpose processor, a conventional processor, a digital signalprocessor (DSP), a plurality of microprocessors, one or moremicroprocessors in association with a DSP core, a controller, amicrocontroller, Application Specific Integrated Circuits (ASICs), FieldProgrammable Gate Array (FPGA) circuits, any other type of integratedcircuit (IC), and/or a state machine, as well as programmable systemdevices (PSDs) employing system-on-chip (SoC) architectures that combinememory functions with programmable logic on a chip that is designed towork with a standard microcontroller. Example memory modules or storagecircuitry may include volatile and/or non-volatile memories such as,e.g., random access memory (RAM), electrically erasable/programmableread-only memories (EEPROMs) or UV-EPROMS, one-time programmable (OTP)memories, Flash memories, static RAM (SRAM), etc.

Further, in at least some additional or alternative implementations, thefunctions/acts described in the blocks may occur out of the order shownin the flowcharts. For example, two blocks shown in succession may infact be executed substantially concurrently or the blocks may sometimesbe executed in the reverse order, depending upon the functionality/actsinvolved. Moreover, the functionality of a given block of the flowchartsand/or block diagrams may be separated into multiple blocks and/or thefunctionality of two or more blocks of the flowcharts and/or blockdiagrams may be at least partially integrated. Also, some blocks in theflowcharts may be optionally omitted. Furthermore, although some of thediagrams include arrows on communication paths to show a primarydirection of communication, it is to be understood that communicationmay occur in the opposite direction relative to the depicted arrows.Finally, other blocks may be added/inserted between the blocks that areillustrated.

It should therefore be clearly understood that the order or sequence ofthe acts, steps, functions, components or blocks illustrated in any ofthe flowcharts depicted in the drawing Figures of the present disclosuremay be modified, altered, replaced, customized or otherwise rearrangedwithin a particular flowchart, including deletion or omission of aparticular act, step, function, component or block. Moreover, the acts,steps, functions, components or blocks illustrated in a particularflowchart may be inter-mixed or otherwise inter-arranged or rearrangedwith the acts, steps, functions, components or blocks illustrated inanother flowchart in order to effectuate additional variations,modifications and configurations with respect to one or more processesfor purposes of practicing the teachings of the present patentdisclosure.

Although various embodiments have been shown and described in detail,the claims are not limited to any particular embodiment or example. Noneof the above Detailed Description should be read as implying that anyparticular component, element, step, act, or function is essential suchthat it must be included in the scope of the claims. Where the phrasessuch as “at least one of A and B” or phrases of similar import arerecited, such a phrase should be understood to mean “only A, only B, orboth A and B.” Reference to an element in the singular is not intendedto mean “one and only one” unless explicitly so stated, but rather “oneor more.” Moreover, the terms “first,” “second,” and “third,” etc.employed in reference to elements or features are used merely as labels,and are not intended to impose numerical requirements, sequentialordering or relative degree of significance or importance on theirobjects. All structural and functional equivalents to the elements ofthe above-described embodiments that are known to those of ordinaryskill in the art are expressly incorporated herein by reference and areintended to be encompassed by the present claims. Accordingly, thoseskilled in the art will recognize that the exemplary embodimentsdescribed herein can be practiced with various modifications andalterations within the spirit and scope of the claims appended below.

1. A computer-implemented method for identifying targets of interest(TOIs) for deep brain stimulation (DBS), the method comprising:obtaining a set of medical imaging data pertaining to human cranialanatomy, the set of medical imaging data sampled from a plurality ofhumans in one or more imaging modalities, wherein the medical imagingdata comprises image slices taken along at least one of coronal,sagittal and axial planes relative to the human cranial anatomy;re-slicing at least a portion of the medical imaging data through one ormore planes that are at an angular orientation with respect to at leastone of the coronal, sagittal and axial planes, thereby obtainingre-sliced medical imaging data; training a first artificial neuralnetwork (ANN) engine using a portion of the medical imaging data thathas not been re-sliced and a portion of the re-sliced medical imagingdata, wherein the medical imaging data is appropriately labeled, toobtain a validated and tested ANN engine configured to identify one ormore TOIs in a human brain image; and executing the first ANN engine, inresponse to an input image of a patient's brain obtained using aparticular imaging modality, to identify at least one particular TOI inthe patient's brain for DBS.
 2. The method as recited in claim 1,further comprising: blending two or more co-registered image slicesselected from at least one of the medical imaging data that has not beenre-sliced or the portion of the re-sliced medical imaging data to obtainhybrid image slices; training the first ANN engine using a portion ofthe hybrid image slices in addition to the medical imaging data that hasnot been re-sliced and the portion of the re-sliced medical imagingdata.
 3. The method as recited in claim 1, further comprising: blendingtwo or more co-registered image slices selected from at least one of themedical imaging data that has not been re-sliced or the portion of there-sliced medical imaging data to obtain hybrid image slices; training asecond ANN engine using a portion of the hybrid image slices to obtain avalidated and tested ANN engine configured to identify one or more TOIsin the human brain image; executing the first and second ANN enginesseparately with respect to the input image of the patient's brain andcombining the TOI identifications obtained respectively therefrom forimproving quality of identification of the at least one particular TOI.4. The method as recited in claim 1, further comprising performing,prior to the training, morphological image processing of image slices ofthe medical imaging data that has not been re-sliced or the portion ofthe re-sliced medical imaging data, wherein the morphological imageprocessing includes at least one of edge detection, contrast boostingand shape detection.
 5. The method as recited in claim 1, furthercomprising performing a dropout technique with respect to the first ANNengine wherein a select number of computational nodes are dropped from aparticular neural network layer in each training epoch.
 6. The method asrecited in claim 1, further comprising: building an electrode scene withrespect to the at least one particular TOI of the patient's brain imagefor placing a DBS lead thereat; and determining an optimal trajectoryfor implanting the DBS lead in the patient's brain relative to aparticular electrode of the DBS lead.
 7. The method as recited in claim6, further comprising: co-registering a computed tomography (CT) imageof the patient's brain with the input image of the patient having the atleast one particular TOI identified for stimulation, wherein the inputimage of the patient's brain comprises one of a pre-operative orintra-operative magnetic resonance imaging (MRI) scan; and obtaining anentry point coordinate set and a target point coordinate set withrespect to the patient's brain for performing an implant procedure toimplant the DBS lead using the optimal trajectory, wherein the entrypoint coordinate set is operative to identify a burr hole location onthe patient's cranium and the target point coordinate set is operativeto identify a location relative to the at least one particular TOI inthe patient's brain.
 8. The method as recited in claim 7, furthercomprising: providing the entry point coordinate set, the target pointcoordinate set and data relating to the optimal trajectory to astereotactic surgery system including a guiding apparatus containing theDBS lead; and automatically guiding the DBS lead to the at least oneparticular TOI based on the entry point coordinate set, the target pointcoordinate set and the data relating to the optimal trajectory data toplace the particular electrode proximate to the at least one particularTOI.
 9. A computer-implemented system configured to facilitateidentifying targets of interest (TOI) for deep brain stimulation (DBS),the system comprising: one or more processors; and a persistent memoryhaving program instructions stored thereon, the program instructions,when executed by the one or more processors, configured to perform:obtaining a set of medical imaging data pertaining to human cranialanatomy, the set of medical imaging data sampled from a plurality ofhumans in one or more imaging modalities, wherein the medical imagingdata comprises image slices taken along at least one of coronal,sagittal and axial planes relative to the human cranial anatomy;re-slicing at least a portion of the medical imaging data through one ormore planes that are at an angular orientation with respect to at leastone of the coronal, sagittal and axial planes, thereby obtainingre-sliced medical imaging data; training a first artificial neuralnetwork (ANN) engine using a portion of the medical imaging data thathas not been re-sliced and a portion of the re-sliced medical imagingdata, wherein the medical imaging data is appropriately labeled, togenerate a validated and tested ANN engine configured to identify one ormore TOIs in a human brain image; and in response to an input image of apatient's brain obtained using a particular imaging modality, executingthe first ANN engine to identify at least one particular TOI in thepatient's brain for DBS.
 10. The system as recited in claim 9, whereinthe program instructions further comprise instructions configured toperform: blending two or more co-registered image slices selected fromat least one of the medical imaging data that has not been re-sliced orthe portion of the re-sliced medical imaging data to generate hybridimage slices; training the first ANN engine using a portion of thehybrid image slices in addition to the medical imaging data that has notbeen re-sliced and the portion of the re-sliced medical imaging data.11. The system as recited in claim 9, wherein the program instructionsfurther comprise instructions configured to perform: blending two ormore co-registered image slices selected from at least one of themedical imaging data that has not been re-sliced or the portion of there-sliced medical imaging data to obtain hybrid image slices; training asecond ANN engine using a portion of the hybrid image slices to generatea validated and tested ANN engine configured to identify one or moreTOIs in the human brain image; executing the first and second ANNengines separately with respect to the input image of the patient'sbrain and combining the TOI identifications obtained respectivelytherefrom for improving quality of identification of the at least oneparticular TOI.
 12. The system as recited in claim 9, wherein theprogram instructions further comprise instructions configured toperform, prior to the training, morphological image processing of imageslices of the medical imaging data that has not been re-sliced or theportion of the re-sliced medical imaging data, wherein the morphologicalimage processing includes at least one of edge detection, contrastboosting and shape detection.
 13. The system as recited in claim 9,wherein the program instructions further comprise instructionsconfigured to perform a dropout technique with respect to the first ANNengine wherein a select number of computational nodes are dropped from aparticular neural network layer in each training epoch.
 14. The systemas recited in claim 9, wherein the program instructions further compriseinstructions configured to perform: building an electrode scene withrespect to the at least one particular TOI of the patient's brain imagefor placing a DBS lead thereat; and determining an optimal trajectoryfor implanting the DBS lead in the patient's brain relative to aparticular electrode of the DBS lead.
 15. The system as recited in claim14, wherein the program instructions further comprise instructionsconfigured to perform: co-registering a computed tomography (CT) imageof the patient's brain with the input image of the patient having the atleast one particular TOI identified for stimulation, wherein the inputimage of the patient's brain comprises one of a pre-operative orintra-operative magnetic resonance imaging (MRI) scan; and determiningan entry point coordinate set and a target point coordinate set withrespect to the patient's brain for performing an implant procedure toimplant the DBS lead using the optimal trajectory, wherein the entrypoint coordinate set is operative to identify a burr hole location onthe patient's cranium and the target point coordinate set is operativeto identify a location relative to the at least one particular TOI inthe patient's brain.
 16. The system as recited in claim 15, furthercomprising a stereotactic surgery system including a guiding apparatuscontaining the DBS lead, and wherein the program instructions furthercomprise instructions configured to perform: providing the entry pointcoordinate set, the target point coordinate set and data relating to theoptimal trajectory to the stereotactic surgery system; and automaticallyguiding the DBS lead to the at least one particular TOI based on theentry point coordinate set, the target point coordinate set and the datarelating to the optimal trajectory data to place the particularelectrode proximate to the at least one particular TOI.